基于Hilbert-Huang变换的局部场电位响应调谐特性研究
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摘要
初级视觉皮层(V1区)是大脑皮层处理视觉信息的第一站,深入分析V1区信号的响应特性对视觉信息处理机制的研究具有深远意义。在该领域的研究大都基于动作电位(spike)和局部场电位(LFP)两种信号。然而spike在检测时易受噪声干扰而造成漏检和误检等问题,严重影响后续分析。因此基于LFP响应特性的研究逐步受到学者们的亲睐。已有研究表明LFP中携带视觉刺激的基本特征等信息,然而对于LFP中具有特征调谐作用的频带范围却没有达成共识。因此准确提取LFP响应频带对进一步从LFP分析视觉皮层信息处理机制奠定了基础。
据此,本文针对LFP信号的非平稳性,基于希尔伯特黄变换(HHT)提取其响应特征频带,研究了其对空间频率和朝向刺激特征的响应调谐特性,并与小波变换进行了对比。主要研究内容如下：
1.信号采集与信号分析。采集并记录了麻醉LE大鼠V1区的spike和LFP信号,研究了LFP信号的特征及常用分析方法,为研究LFP特征调谐性能奠定了先验知识基础。
2.基于小波变换研究LFP的响应调谐特性。运用小波分解提取LFP的响应频带Gamma频带,研究了Gamma频带对空间频率和朝向视觉特征的响应特性,并且与同一电极记录到的spike信号进行了对比。结果表明Gamma频带能量对不同视觉刺激特征具有不同的响应,与spike信号的调谐特性具有较好一致性。但是,小波变换结果依赖于小波基函数的选择,这是小波变换分析信号的难点和缺点所在。
3.基于HHT方法研究LFP的响应调谐特性。该方法对LFP的整个频带的调谐性能都进行了研究,而不局限于对LFP局部频带的研究。首先将LFP分解为若干固有模态分量(IMF),据此分析了各阶固有模态分量对空间频率和朝向视觉刺激的响应调谐特性。结果表明：相比其他IMF,第二阶固有模态分量对视觉刺激特征的调谐特性最强,并且与spike信号的调谐特性具有较好一致性。该方法避免了传统小波算法寻找合适小波基的麻烦,更加准确的给出了响应特征,为LFP信号的特征提取及后续研究提供了有效手段,具有较高的推广价值。
4.对基于小波变换和HHT两种方法得到的结果进行对比,分析了两种方法对空间频率和朝向光栅刺激的调谐性能。结果表明基于HHT方法得到的调谐指数的均值高于小波变换。因此,基于HHT方法在LFP响应频带特征提取上更有优势。
The primary visual cortex is the first station of visual processing mechanism in cerebral cortex, and it is significance to in-depth analysis of response characteristics of signals for study visual processing mechanism in primary visual cortex. Most of the researches were based on the activity potential (spike) and local filed potential (LFP) recorded from primary visual cortex. However spike is susceptible to noise interference when it was detected, which resulting in missed and false detection, and the subsequent analysis was affected seriously. Therefore, research of response characteristics based on LFP was gradually studied by researchers. Studied have shown that visual stimulation was encoded by LFP, however, no coherent conclusion exists about which frequency ranges exhibit feature tuning. Extracting of response ranges of LFP has far-reaching significance to study visual processing mechanism.
Addressing this, the response characteristic frequency bands of LFP which is non-stationary signal were extracted based on Hilbert-Huang Transform (HHT) in this paper, and response tuning characteristics underlying the stimuli of spatial frequency and orientation were researched, and wavelet transform was compared. The main contents are as follows:
1. Signal acquisition and signal analysis. Spike and LFP signal were collected and recorded from anaesthetic Long Evans rat, and the characteristics of LFP and analysis methods used commonly were expounded, which laid the foundation for the study of tuning characteristics of LFP.
2. Response characteristics of local field potential based on wavelet transform was researched. We studied the response characteristics for spatial frequency and orientation according to the Gamma band of LFP which extracted by wavelet decomposition, and the spike recorded from the same electrode was compared. The results showed that the Gamma band reflected different response to different visual stimuli, and it was consistent with spike. However, wavelet transform depends on the choice of the wavelet function which is the difficulties and disadvantages of wavelet transform analysis.
3. Response characteristics of local field potential based on Hilbert-Huang transform was researched. In contrast to previous studies, which often reported correlates of visual processing only in a limited frequency range of LFP, here the entire frequency range of LFP were studied by HHT in visual processing. The response characteristics for spatial frequency and orientation were studied based on the intrinsic mode functions of LFP which decomposed by HHT. The results showed that the second intrinsic mode function reflected response to visual stimuli compared with the other intrinsic mode functions, and it was consistent with spike. The method avoids the trouble of the wavelet transform which requires a proper wavelet basis, and a more accurate response characteristic was obtained, providing an effective means for feature extraction and the follow-up study of LFP, and it has a high value of promotion.
4. The tuning characteristics of LFP underlying the stimuli of spatial frequency and orientation based on wavelet transform and HHT was compared. The results showed that the average tuning index obtained from HHT is higher than wavelet transform. Therefore, extracting response band of LFP based on HHT is more advantages.
据此,本文针对LFP信号的非平稳性,基于希尔伯特黄变换(HHT)提取其响应特征频带,研究了其对空间频率和朝向刺激特征的响应调谐特性,并与小波变换进行了对比。主要研究内容如下：
1.信号采集与信号分析。采集并记录了麻醉LE大鼠V1区的spike和LFP信号,研究了LFP信号的特征及常用分析方法,为研究LFP特征调谐性能奠定了先验知识基础。
2.基于小波变换研究LFP的响应调谐特性。运用小波分解提取LFP的响应频带Gamma频带,研究了Gamma频带对空间频率和朝向视觉特征的响应特性,并且与同一电极记录到的spike信号进行了对比。结果表明Gamma频带能量对不同视觉刺激特征具有不同的响应,与spike信号的调谐特性具有较好一致性。但是,小波变换结果依赖于小波基函数的选择,这是小波变换分析信号的难点和缺点所在。
3.基于HHT方法研究LFP的响应调谐特性。该方法对LFP的整个频带的调谐性能都进行了研究,而不局限于对LFP局部频带的研究。首先将LFP分解为若干固有模态分量(IMF),据此分析了各阶固有模态分量对空间频率和朝向视觉刺激的响应调谐特性。结果表明：相比其他IMF,第二阶固有模态分量对视觉刺激特征的调谐特性最强,并且与spike信号的调谐特性具有较好一致性。该方法避免了传统小波算法寻找合适小波基的麻烦,更加准确的给出了响应特征,为LFP信号的特征提取及后续研究提供了有效手段,具有较高的推广价值。
4.对基于小波变换和HHT两种方法得到的结果进行对比,分析了两种方法对空间频率和朝向光栅刺激的调谐性能。结果表明基于HHT方法得到的调谐指数的均值高于小波变换。因此,基于HHT方法在LFP响应频带特征提取上更有优势。
The primary visual cortex is the first station of visual processing mechanism in cerebral cortex, and it is significance to in-depth analysis of response characteristics of signals for study visual processing mechanism in primary visual cortex. Most of the researches were based on the activity potential (spike) and local filed potential (LFP) recorded from primary visual cortex. However spike is susceptible to noise interference when it was detected, which resulting in missed and false detection, and the subsequent analysis was affected seriously. Therefore, research of response characteristics based on LFP was gradually studied by researchers. Studied have shown that visual stimulation was encoded by LFP, however, no coherent conclusion exists about which frequency ranges exhibit feature tuning. Extracting of response ranges of LFP has far-reaching significance to study visual processing mechanism.
Addressing this, the response characteristic frequency bands of LFP which is non-stationary signal were extracted based on Hilbert-Huang Transform (HHT) in this paper, and response tuning characteristics underlying the stimuli of spatial frequency and orientation were researched, and wavelet transform was compared. The main contents are as follows:
1. Signal acquisition and signal analysis. Spike and LFP signal were collected and recorded from anaesthetic Long Evans rat, and the characteristics of LFP and analysis methods used commonly were expounded, which laid the foundation for the study of tuning characteristics of LFP.
2. Response characteristics of local field potential based on wavelet transform was researched. We studied the response characteristics for spatial frequency and orientation according to the Gamma band of LFP which extracted by wavelet decomposition, and the spike recorded from the same electrode was compared. The results showed that the Gamma band reflected different response to different visual stimuli, and it was consistent with spike. However, wavelet transform depends on the choice of the wavelet function which is the difficulties and disadvantages of wavelet transform analysis.
3. Response characteristics of local field potential based on Hilbert-Huang transform was researched. In contrast to previous studies, which often reported correlates of visual processing only in a limited frequency range of LFP, here the entire frequency range of LFP were studied by HHT in visual processing. The response characteristics for spatial frequency and orientation were studied based on the intrinsic mode functions of LFP which decomposed by HHT. The results showed that the second intrinsic mode function reflected response to visual stimuli compared with the other intrinsic mode functions, and it was consistent with spike. The method avoids the trouble of the wavelet transform which requires a proper wavelet basis, and a more accurate response characteristic was obtained, providing an effective means for feature extraction and the follow-up study of LFP, and it has a high value of promotion.
4. The tuning characteristics of LFP underlying the stimuli of spatial frequency and orientation based on wavelet transform and HHT was compared. The results showed that the average tuning index obtained from HHT is higher than wavelet transform. Therefore, extracting response band of LFP based on HHT is more advantages.
引文
[1]罗四维.视觉信息认知计算理论[M].北京：科学出版社.2010.
[2]Mazer JA, Vinje WE, McDermott J, et al. Spatial frequency and orientation tuning dynamics in area V1 [J]. Neurobiology,2002,99(3):1645～1650
[3]Girman SV, Sauve Y, Lund RD. Receptive Field Properties of Single Neurons in Rat Primary Visual Cortex [J].1999, Neurophysiol,82:301～311
[4]Gray CM, Konig P, Engel AK, et al. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties [J]. Nature,1989, 338:334～38
[5]Sirovich L, Uglesich R. The organization of orientation and spatial frequency in primary visual cortex[J]. Proceedings of the National Academy of Science of the United States of America,2004,101(48):16941～16946
[6]Shevelev I A, Sharaev GA, Lazareva NA, et al. Dynamics of orientation tuning in the cat striate cortex neurons [J]. Neuroscience,1993,56(4):865～876
[7]Tardif E, Bergeron A, Lepore F. Spatial and temporal frequency tuning and contrast sensitivity of single neurons in area 21a of the cat [J]. Brain Research,1996, 716:219～223
[8]Molotchnikoff S, Gillet PC, Shumikhina S, et al. Spatial frequency characteristics of nearby neurons in cats'visual cortex [J]. Neuroscience Letters,2007,418(3):242～247
[9]Belitski A, Panzeri S, Magri C, et al. Sensory information in local field potentials and spikes from visual and auditory cortices:time scales and frequency bands [J]. Journal Computational Neuroscience,2010,29(3):533～545
[10]Nagamine, Janet MD, Hill, et al. Combined therapy with zaprinast and inhaled nitric oxide abolishes hypoxic pulmonary hypertension [J]. Critical Care Medicine,2008, 28(7):2420～2424
[11]柳冬梅,大鼠压杆过程的局部场电位分析.[硕士学位论文].浙江：浙江大学,2008
[12]Brosch M, Budinger E, Scheich H. Stimulus-related gamma oscillations in primate auditory cortex [J]. Neurophysiol,2002,87(6):2715～2725
[13]Rols G, Tallon-Baudry C, Girard P, et al. Cortical mapping of gamma oscillations in areas V1 and V4 of the macaque monkey[J]. Vis.Neurosci,2001,18(4):527～540
[14]Eckhorn R, Frien A, Bauer R. High frequency(60-90Hz) ocillations in primary visual cortex of awake monkey [J]. Neuroreport,1993,4(3):243～246
[15]Gawne TJ. The local and nonlocal components of the local field potential in awake primate visual cortex [J]. Journal of Computational Neuroscience,2010,29(3):615～623
[16]Ince RAA, Mazzoni A, Bartels A, et al. A novel test to determine the significance of neural selectivity to single and multiple potentially correlated stimulus features [J]. Journal of Neuroscience Methods,2012,210(1):49～65
[17]Belitski A, Gretton A, Magri C. Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information [J]. The Journal of Neuroscience,2008,28(22):5696～5709
[18]Siegel M, K6nig P. Frequency distribution of stimulus dependent synchronization in area 18 of the awake cat [J]. The Journal of Neuroscience,2003,23(10):4251～4260
[19]Frien A, Eckhorn R, Bauer R, et al. Fast oscillations display sharper orientation tuning than slower components of the same recordings in striate cortex of the awake monkey [J]. European Journal of Neuroscience,2000,12(4):1453～1465
[20]Chalk M, Herrero JL, Gieselmann MA, et al. Attention reduces stimulus-driven gamma frequency oscillations and spike field coherence in V1 [J]. Neuron,2010, 66(1-2):114～125
[21]Liu Jing, Local field potential in cortical area MT:stimulus tuning and behavioral correlations[J]. The Journal of Neuroscience,2006,26(30):7779～7790
[22]Singer W, Gray CM. Visual feature integration and the temporal correlation hypothesis. Annu. Rev. Neurosci,1995,18:555～586
[23]Henrie JA, Shapley R. LFP power spectra in V1 cortex:the graded effect of stimulus contrast [J]. Journal of Neurophysiology,2005,94(1):479～490
[24]Kayser C, Konig P. Stimulus locking and feature selectivity prevail in complementary frequency ranges of V1 local field potentials [J]. Neuroscience,2004,19(2):485～489
[25]Philipp B, Georgios A. Comparing the feature selectivity of the gamma-band of the local field potential and the underlying spiking activity in primate visual cortex [J]. Frontiers in Systems Neuroscience,2008,2(2):1～11
[26]Cesare Magri, Alberto Mazzoni, Nikos K. Optimal band separation of extracellular field potentials[J]. Journal of Neuroscience Methods,2011:1～13
[27]Siegel M, Konig P. A functional gamma-band defined by stimulus-dependent syn-chronization in area 18 of awake behaving cats [J]. Neurosci,2003,23:4251～60
[28]Belitski A, Gretton A, Magri C, et al. Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information [J]. Neurosci,2008, 28:5696～5709
[29]Magri C, Mazzoni A, Logothetis NK, et al. Optimal band separation of extracellular field potentials [J]. Tournal of Neuroscience Methods,2012,210(1),66～78
[30]Huang NE, Shen Z, Long SR, et al. The empirical mode decomposition and the hilbert spectrum for nonlinear and non-station time series analysis [J]. Proceedings of the Royal Society of London,1998,454:903～995
[31]Andrea P, Adenauer GC, Silvia C. Time-freqency spectral analysis of TMS-evoked EEG oscillations by means of Hilbert-Huang transform[J]. Journal of Neuroscience Methods, 2011,198:236～245
[32]Sweeney CM, Nasuto SJ. A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition [J]. Journal of Computational Neuroscience,2007, 23(1):79～111
[33]袁玲,杨帮华,马世伟.基于HHT和SVM的运动想象脑电识别[J].仪器仪表学报,2010,31(3)：649～654
[34]Lianga H, Steven LB, Desimonec R. Empirical mode decomposition:a method for analyzing neural data[J]. Neurocomputing,2005,65～66:801-807
[35]Cong F, Member. Concatenated Trial based Hilbert-Huang Transformation on Event Related Potentials[J]. IEEE,2010
[36]Molteni E, Ferrari M, Preatoni E, et al. Event Related Synchronization and Hilbert Huang Transform in the study of motor Adaptation:A Comparison of Methods[J]. IEEE, 2011,132～135
[37]Wild J, Prekopcsak Z, Sieger T, et al. Performance comparison of extracellular spike sorting algorithms for single channel recordings[J]. Neurosci Meth,2012,203(2): 369～376
[38]姚军财,基于人眼感知特性的图像空间频率计算方法[J].陕西理工学院学报(自然科学版,2012,28(5)：30～33
[39]裴强,胡波Hilbert-Huang变换方法研究进展[J].世界地震工程,2011,27(2)：21-29
[40]邱天爽.统计信号处理医学信号分析与处理[M].北京：科学出版社.2012
[41]孙延奎.小波分析及其应用[M].北京：机械工业出版社,2005
[42]毛源.多通道局部场电位及其分量相位同步振荡模式对刺激编码的研究.[硕士学位论文].天津：天津医科大学,2009
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[45]尚志刚,冯平艳,刘新玉等.局部场电位丫频带能量对朝向调谐特性研究[J].郑州大学学报(工学版),2012,33(6)：28～32
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[47]Hassan SHARABATY, Bruno JAMMES, Daniel ESTEVE. EEG analysis using HHT: One step toward automatic drowsiness scoring[J]. IEEE,2008
[2]Mazer JA, Vinje WE, McDermott J, et al. Spatial frequency and orientation tuning dynamics in area V1 [J]. Neurobiology,2002,99(3):1645～1650
[3]Girman SV, Sauve Y, Lund RD. Receptive Field Properties of Single Neurons in Rat Primary Visual Cortex [J].1999, Neurophysiol,82:301～311
[4]Gray CM, Konig P, Engel AK, et al. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties [J]. Nature,1989, 338:334～38
[5]Sirovich L, Uglesich R. The organization of orientation and spatial frequency in primary visual cortex[J]. Proceedings of the National Academy of Science of the United States of America,2004,101(48):16941～16946
[6]Shevelev I A, Sharaev GA, Lazareva NA, et al. Dynamics of orientation tuning in the cat striate cortex neurons [J]. Neuroscience,1993,56(4):865～876
[7]Tardif E, Bergeron A, Lepore F. Spatial and temporal frequency tuning and contrast sensitivity of single neurons in area 21a of the cat [J]. Brain Research,1996, 716:219～223
[8]Molotchnikoff S, Gillet PC, Shumikhina S, et al. Spatial frequency characteristics of nearby neurons in cats'visual cortex [J]. Neuroscience Letters,2007,418(3):242～247
[9]Belitski A, Panzeri S, Magri C, et al. Sensory information in local field potentials and spikes from visual and auditory cortices:time scales and frequency bands [J]. Journal Computational Neuroscience,2010,29(3):533～545
[10]Nagamine, Janet MD, Hill, et al. Combined therapy with zaprinast and inhaled nitric oxide abolishes hypoxic pulmonary hypertension [J]. Critical Care Medicine,2008, 28(7):2420～2424
[11]柳冬梅,大鼠压杆过程的局部场电位分析.[硕士学位论文].浙江：浙江大学,2008
[12]Brosch M, Budinger E, Scheich H. Stimulus-related gamma oscillations in primate auditory cortex [J]. Neurophysiol,2002,87(6):2715～2725
[13]Rols G, Tallon-Baudry C, Girard P, et al. Cortical mapping of gamma oscillations in areas V1 and V4 of the macaque monkey[J]. Vis.Neurosci,2001,18(4):527～540
[14]Eckhorn R, Frien A, Bauer R. High frequency(60-90Hz) ocillations in primary visual cortex of awake monkey [J]. Neuroreport,1993,4(3):243～246
[15]Gawne TJ. The local and nonlocal components of the local field potential in awake primate visual cortex [J]. Journal of Computational Neuroscience,2010,29(3):615～623
[16]Ince RAA, Mazzoni A, Bartels A, et al. A novel test to determine the significance of neural selectivity to single and multiple potentially correlated stimulus features [J]. Journal of Neuroscience Methods,2012,210(1):49～65
[17]Belitski A, Gretton A, Magri C. Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information [J]. The Journal of Neuroscience,2008,28(22):5696～5709
[18]Siegel M, K6nig P. Frequency distribution of stimulus dependent synchronization in area 18 of the awake cat [J]. The Journal of Neuroscience,2003,23(10):4251～4260
[19]Frien A, Eckhorn R, Bauer R, et al. Fast oscillations display sharper orientation tuning than slower components of the same recordings in striate cortex of the awake monkey [J]. European Journal of Neuroscience,2000,12(4):1453～1465
[20]Chalk M, Herrero JL, Gieselmann MA, et al. Attention reduces stimulus-driven gamma frequency oscillations and spike field coherence in V1 [J]. Neuron,2010, 66(1-2):114～125
[21]Liu Jing, Local field potential in cortical area MT:stimulus tuning and behavioral correlations[J]. The Journal of Neuroscience,2006,26(30):7779～7790
[22]Singer W, Gray CM. Visual feature integration and the temporal correlation hypothesis. Annu. Rev. Neurosci,1995,18:555～586
[23]Henrie JA, Shapley R. LFP power spectra in V1 cortex:the graded effect of stimulus contrast [J]. Journal of Neurophysiology,2005,94(1):479～490
[24]Kayser C, Konig P. Stimulus locking and feature selectivity prevail in complementary frequency ranges of V1 local field potentials [J]. Neuroscience,2004,19(2):485～489
[25]Philipp B, Georgios A. Comparing the feature selectivity of the gamma-band of the local field potential and the underlying spiking activity in primate visual cortex [J]. Frontiers in Systems Neuroscience,2008,2(2):1～11
[26]Cesare Magri, Alberto Mazzoni, Nikos K. Optimal band separation of extracellular field potentials[J]. Journal of Neuroscience Methods,2011:1～13
[27]Siegel M, Konig P. A functional gamma-band defined by stimulus-dependent syn-chronization in area 18 of awake behaving cats [J]. Neurosci,2003,23:4251～60
[28]Belitski A, Gretton A, Magri C, et al. Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information [J]. Neurosci,2008, 28:5696～5709
[29]Magri C, Mazzoni A, Logothetis NK, et al. Optimal band separation of extracellular field potentials [J]. Tournal of Neuroscience Methods,2012,210(1),66～78
[30]Huang NE, Shen Z, Long SR, et al. The empirical mode decomposition and the hilbert spectrum for nonlinear and non-station time series analysis [J]. Proceedings of the Royal Society of London,1998,454:903～995
[31]Andrea P, Adenauer GC, Silvia C. Time-freqency spectral analysis of TMS-evoked EEG oscillations by means of Hilbert-Huang transform[J]. Journal of Neuroscience Methods, 2011,198:236～245
[32]Sweeney CM, Nasuto SJ. A novel approach to the detection of synchronisation in EEG based on empirical mode decomposition [J]. Journal of Computational Neuroscience,2007, 23(1):79～111
[33]袁玲,杨帮华,马世伟.基于HHT和SVM的运动想象脑电识别[J].仪器仪表学报,2010,31(3)：649～654
[34]Lianga H, Steven LB, Desimonec R. Empirical mode decomposition:a method for analyzing neural data[J]. Neurocomputing,2005,65～66:801-807
[35]Cong F, Member. Concatenated Trial based Hilbert-Huang Transformation on Event Related Potentials[J]. IEEE,2010
[36]Molteni E, Ferrari M, Preatoni E, et al. Event Related Synchronization and Hilbert Huang Transform in the study of motor Adaptation:A Comparison of Methods[J]. IEEE, 2011,132～135
[37]Wild J, Prekopcsak Z, Sieger T, et al. Performance comparison of extracellular spike sorting algorithms for single channel recordings[J]. Neurosci Meth,2012,203(2): 369～376
[38]姚军财,基于人眼感知特性的图像空间频率计算方法[J].陕西理工学院学报(自然科学版,2012,28(5)：30～33
[39]裴强,胡波Hilbert-Huang变换方法研究进展[J].世界地震工程,2011,27(2)：21-29
[40]邱天爽.统计信号处理医学信号分析与处理[M].北京：科学出版社.2012
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[42]毛源.多通道局部场电位及其分量相位同步振荡模式对刺激编码的研究.[硕士学位论文].天津：天津医科大学,2009
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